Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2009.03106.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:09:01.833903Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T21:06:04.272063Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation d17ab64f-6d48-4427-a0f7-d7440542f00c · inbound
Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning cites this paper.
Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:09:01.833903Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a60775cc-982d-4339-b934-7278a850ed2f · inbound
FlashDP: Private Training Large Language Models with Efficient DP-SGD cites this paper.
FlashDP: Private Training Large Language Models with Efficient DP-SGD Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:06:04.344285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5aeb9500-0844-4d4f-ad41-ffc288b1c839 · inbound
Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail cites this paper.
Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T04:58:48.448201Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.